Abstract
Commonly, information systems are organized by the use of tables that are composed of a fixed number of columns representing the information system’s attributes. However, in a typical hospital scenario, patients may have a variable number of diagnoses and this data is recorded in the patients’ medical records in a random order. Treatments are prescribed based on these diagnoses, which makes it harder to mine meta-actions from healthcare datasets. In such scenario, the patients are not necessarily followed for a specific disease, but are treated for what they are diagnosed for. This makes it even more complex to prescribe personalized treatments since patients react differently to treatments based on their state (diagnoses). In this work, we present a method to extract personalized meta-actions from surgical datasets with variable number of diagnoses. We used the Florida State Inpatient Databases (SID), which is a part of the Healthcare Cost and Utilization Project (HCUP) [1] to demonstrate how to extract meta-actions and evaluate them.
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References
Clinical classifications software (ccs) for icd-9-cm, http://www.hcup-us.ahrq.gov
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Touati, H., Raś, Z.W., Studnicki, J., Wieczorkowska, A.A. (2014). Mining Surgical Meta-actions Effects with Variable Diagnoses’ Number. In: Andreasen, T., Christiansen, H., Cubero, JC., Raś, Z.W. (eds) Foundations of Intelligent Systems. ISMIS 2014. Lecture Notes in Computer Science(), vol 8502. Springer, Cham. https://doi.org/10.1007/978-3-319-08326-1_26
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DOI: https://doi.org/10.1007/978-3-319-08326-1_26
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